Overview

Dataset statistics

Number of variables33
Number of observations279712
Missing cells1286427
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory363.5 MiB
Average record size in memory1.3 KiB

Variable types

Numeric19
Text5
DateTime1
Categorical4
Boolean4

Alerts

listing_id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with listing_idHigh correlation
host_response_rate is highly overall correlated with host_response_timeHigh correlation
latitude is highly overall correlated with cityHigh correlation
longitude is highly overall correlated with district and 1 other fieldsHigh correlation
accommodates is highly overall correlated with bedroomsHigh correlation
bedrooms is highly overall correlated with accommodatesHigh correlation
price is highly overall correlated with districtHigh correlation
review_scores_rating is highly overall correlated with review_scores_accuracy and 2 other fieldsHigh correlation
review_scores_accuracy is highly overall correlated with review_scores_rating and 3 other fieldsHigh correlation
review_scores_cleanliness is highly overall correlated with review_scores_rating and 2 other fieldsHigh correlation
review_scores_checkin is highly overall correlated with review_scores_communicationHigh correlation
review_scores_communication is highly overall correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_value is highly overall correlated with review_scores_rating and 2 other fieldsHigh correlation
host_response_time is highly overall correlated with host_response_rateHigh correlation
district is highly overall correlated with longitude and 2 other fieldsHigh correlation
city is highly overall correlated with latitude and 2 other fieldsHigh correlation
host_has_profile_pic is highly imbalanced (96.8%)Imbalance
host_response_time has 128782 (46.0%) missing valuesMissing
host_response_rate has 128782 (46.0%) missing valuesMissing
host_acceptance_rate has 113087 (40.4%) missing valuesMissing
district has 242700 (86.8%) missing valuesMissing
bedrooms has 29435 (10.5%) missing valuesMissing
review_scores_rating has 91405 (32.7%) missing valuesMissing
review_scores_accuracy has 91713 (32.8%) missing valuesMissing
review_scores_cleanliness has 91665 (32.8%) missing valuesMissing
review_scores_checkin has 91771 (32.8%) missing valuesMissing
review_scores_communication has 91687 (32.8%) missing valuesMissing
review_scores_location has 91775 (32.8%) missing valuesMissing
review_scores_value has 91785 (32.8%) missing valuesMissing
host_total_listings_count is highly skewed (γ1 = 23.49009442)Skewed
price is highly skewed (γ1 = 61.61617815)Skewed
minimum_nights is highly skewed (γ1 = 123.1708696)Skewed
maximum_nights is highly skewed (γ1 = 284.2206534)Skewed
listing_id has unique valuesUnique
host_response_rate has 10640 (3.8%) zerosZeros
host_acceptance_rate has 11431 (4.1%) zerosZeros
host_total_listings_count has 33265 (11.9%) zerosZeros

Reproduction

Analysis started2023-10-14 09:18:23.434867
Analysis finished2023-10-14 09:19:12.930235
Duration49.5 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

listing_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct279712
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26381955
Minimum2577
Maximum48343530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:12.980658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2577
5-th percentile2600270.9
Q113844619
median27670985
Q339784851
95-th percentile46535077
Maximum48343530
Range48340953
Interquartile range (IQR)25940232

Descriptive statistics

Standard deviation14425759
Coefficient of variation (CV)0.546804
Kurtosis-1.2527201
Mean26381955
Median Absolute Deviation (MAD)12789516
Skewness-0.1930976
Sum7.3793495 × 1012
Variance2.0810251 × 1014
MonotonicityNot monotonic
2023-10-14T06:19:13.040969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281420 1
 
< 0.1%
18186014 1
 
< 0.1%
40727670 1
 
< 0.1%
39400416 1
 
< 0.1%
34469158 1
 
< 0.1%
30879584 1
 
< 0.1%
21095360 1
 
< 0.1%
18967318 1
 
< 0.1%
15932792 1
 
< 0.1%
43542311 1
 
< 0.1%
Other values (279702) 279702
> 99.9%
ValueCountFrequency (%)
2577 1
< 0.1%
2595 1
< 0.1%
2737 1
< 0.1%
2903 1
< 0.1%
3079 1
< 0.1%
3109 1
< 0.1%
3191 1
< 0.1%
3831 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
ValueCountFrequency (%)
48343530 1
< 0.1%
48339877 1
< 0.1%
48339041 1
< 0.1%
48339035 1
< 0.1%
48338892 1
< 0.1%
48338514 1
< 0.1%
48338475 1
< 0.1%
48338365 1
< 0.1%
48330766 1
< 0.1%
48330345 1
< 0.1%

name
Text

Distinct265838
Distinct (%)95.1%
Missing175
Missing (%)0.1%
Memory size28.0 MiB
2023-10-14T06:19:13.244404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length255
Median length180
Mean length36.224214
Min length1

Characters and Unicode

Total characters10126008
Distinct characters2661
Distinct categories24 ?
Distinct scripts15 ?
Distinct blocks37 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258704 ?
Unique (%)92.5%

Sample

1st rowBeautiful Flat in le Village Montmartre, Paris
2nd row39 m² Paris (Sacre Cœur)
3rd rowLovely apartment with Terrace, 60m2
4th rowCosy studio (close to Eiffel tower)
5th rowClose to Eiffel Tower - Beautiful flat : 2 rooms
ValueCountFrequency (%)
53434
 
3.2%
in 52229
 
3.2%
apartment 33633
 
2.0%
room 33255
 
2.0%
the 23452
 
1.4%
studio 21345
 
1.3%
with 17712
 
1.1%
2 16837
 
1.0%
paris 16166
 
1.0%
de 15984
 
1.0%
Other values (79848) 1361858
82.7%
2023-10-14T06:19:13.612853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1380403
 
13.6%
e 785155
 
7.8%
a 745156
 
7.4%
o 624907
 
6.2%
t 572056
 
5.6%
r 520408
 
5.1%
i 512966
 
5.1%
n 503348
 
5.0%
l 292061
 
2.9%
s 291905
 
2.9%
Other values (2651) 3897643
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6831127
67.5%
Uppercase Letter 1385201
 
13.7%
Space Separator 1380575
 
13.6%
Decimal Number 173874
 
1.7%
Other Punctuation 162244
 
1.6%
Other Letter 97266
 
1.0%
Dash Punctuation 48999
 
0.5%
Close Punctuation 11959
 
0.1%
Open Punctuation 11140
 
0.1%
Math Symbol 10848
 
0.1%
Other values (14) 12775
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2665
 
2.7%
1569
 
1.6%
1555
 
1.6%
1366
 
1.4%
1337
 
1.4%
1300
 
1.3%
1250
 
1.3%
1233
 
1.3%
1154
 
1.2%
1048
 
1.1%
Other values (1933) 82789
85.1%
Other Symbol
ValueCountFrequency (%)
2286
40.3%
433
 
7.6%
367
 
6.5%
308
 
5.4%
° 272
 
4.8%
98
 
1.7%
🌟 80
 
1.4%
79
 
1.4%
77
 
1.4%
44
 
0.8%
Other values (306) 1622
28.6%
Lowercase Letter
ValueCountFrequency (%)
e 785155
11.5%
a 745156
10.9%
o 624907
 
9.1%
t 572056
 
8.4%
r 520408
 
7.6%
i 512966
 
7.5%
n 503348
 
7.4%
l 292061
 
4.3%
s 291905
 
4.3%
m 281495
 
4.1%
Other values (103) 1701670
24.9%
Uppercase Letter
ValueCountFrequency (%)
A 123431
 
8.9%
C 120089
 
8.7%
S 119153
 
8.6%
B 104786
 
7.6%
R 94720
 
6.8%
P 83823
 
6.1%
T 83251
 
6.0%
E 74690
 
5.4%
M 69903
 
5.0%
L 67142
 
4.8%
Other values (76) 444213
32.1%
Other Punctuation
ValueCountFrequency (%)
, 45847
28.3%
. 26777
16.5%
/ 25532
15.7%
! 20765
12.8%
& 15085
 
9.3%
' 7075
 
4.4%
* 4204
 
2.6%
: 3941
 
2.4%
" 3570
 
2.2%
@ 2985
 
1.8%
Other values (28) 6463
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 42734
24.6%
1 41307
23.8%
0 21869
12.6%
3 17965
10.3%
5 15174
 
8.7%
4 12789
 
7.4%
6 7244
 
4.2%
7 5697
 
3.3%
8 5049
 
2.9%
9 3998
 
2.3%
Other values (16) 48
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 6505
60.0%
| 3015
27.8%
~ 644
 
5.9%
> 204
 
1.9%
= 148
 
1.4%
< 129
 
1.2%
56
 
0.5%
51
 
0.5%
19
 
0.2%
14
 
0.1%
Other values (16) 63
 
0.6%
Other Number
ValueCountFrequency (%)
² 1168
95.6%
7
 
0.6%
7
 
0.6%
6
 
0.5%
5
 
0.4%
5
 
0.4%
3
 
0.2%
3
 
0.2%
3
 
0.2%
½ 2
 
0.2%
Other values (12) 13
 
1.1%
Nonspacing Mark
ValueCountFrequency (%)
581
20.4%
418
14.6%
344
12.1%
338
11.8%
334
11.7%
333
11.7%
227
 
8.0%
80
 
2.8%
58
 
2.0%
57
 
2.0%
Other values (10) 84
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 10416
93.5%
[ 354
 
3.2%
269
 
2.4%
26
 
0.2%
25
 
0.2%
20
 
0.2%
{ 11
 
0.1%
11
 
0.1%
6
 
0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10898
91.1%
385
 
3.2%
] 359
 
3.0%
263
 
2.2%
25
 
0.2%
} 13
 
0.1%
11
 
0.1%
3
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Modifier Symbol
ValueCountFrequency (%)
^ 96
38.9%
´ 78
31.6%
¨ 43
17.4%
` 22
 
8.9%
¸ 2
 
0.8%
˙ 2
 
0.8%
2
 
0.8%
˚ 2
 
0.8%
Currency Symbol
ValueCountFrequency (%)
$ 267
77.4%
38
 
11.0%
¤ 34
 
9.9%
3
 
0.9%
¢ 1
 
0.3%
¥ 1
 
0.3%
฿ 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 48554
99.1%
288
 
0.6%
133
 
0.3%
22
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1380403
> 99.9%
  125
 
< 0.1%
24
 
< 0.1%
  23
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
181
68.0%
« 45
 
16.9%
40
 
15.0%
Format
ValueCountFrequency (%)
145
94.2%
8
 
5.2%
1
 
0.6%
Final Punctuation
ValueCountFrequency (%)
1286
96.6%
» 45
 
3.4%
Connector Punctuation
ValueCountFrequency (%)
_ 601
99.7%
2
 
0.3%
Modifier Letter
ValueCountFrequency (%)
44
89.8%
5
 
10.2%
Private Use
ValueCountFrequency (%)
1
50.0%
󰀄 1
50.0%
Enclosing Mark
ValueCountFrequency (%)
21
100.0%
Control
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8213877
81.1%
Common 1809492
 
17.9%
Han 81507
 
0.8%
Thai 13575
 
0.1%
Arabic 3822
 
< 0.1%
Cyrillic 2484
 
< 0.1%
Hangul 606
 
< 0.1%
Katakana 467
 
< 0.1%
Inherited 89
 
< 0.1%
Hiragana 35
 
< 0.1%
Other values (5) 54
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
2665
 
3.3%
1569
 
1.9%
1555
 
1.9%
1366
 
1.7%
1337
 
1.6%
1300
 
1.6%
1250
 
1.5%
1233
 
1.5%
1048
 
1.3%
1016
 
1.2%
Other values (1587) 67168
82.4%
Common
ValueCountFrequency (%)
1380403
76.3%
- 48554
 
2.7%
, 45847
 
2.5%
2 42734
 
2.4%
1 41307
 
2.3%
. 26777
 
1.5%
/ 25532
 
1.4%
0 21869
 
1.2%
! 20765
 
1.1%
3 17965
 
1.0%
Other values (464) 137739
 
7.6%
Hangul
ValueCountFrequency (%)
26
 
4.3%
23
 
3.8%
20
 
3.3%
18
 
3.0%
17
 
2.8%
15
 
2.5%
15
 
2.5%
14
 
2.3%
13
 
2.1%
12
 
2.0%
Other values (170) 433
71.5%
Latin
ValueCountFrequency (%)
e 785155
 
9.6%
a 745156
 
9.1%
o 624907
 
7.6%
t 572056
 
7.0%
r 520408
 
6.3%
i 512966
 
6.2%
n 503348
 
6.1%
l 292061
 
3.6%
s 291905
 
3.6%
m 281495
 
3.4%
Other values (125) 3084420
37.6%
Thai
ValueCountFrequency (%)
1154
 
8.5%
977
 
7.2%
879
 
6.5%
793
 
5.8%
581
 
4.3%
546
 
4.0%
508
 
3.7%
453
 
3.3%
440
 
3.2%
429
 
3.2%
Other values (56) 6815
50.2%
Katakana
ValueCountFrequency (%)
45
 
9.6%
29
 
6.2%
29
 
6.2%
23
 
4.9%
22
 
4.7%
20
 
4.3%
18
 
3.9%
15
 
3.2%
15
 
3.2%
14
 
3.0%
Other values (55) 237
50.7%
Cyrillic
ValueCountFrequency (%)
а 290
 
11.7%
т 210
 
8.5%
о 186
 
7.5%
р 160
 
6.4%
н 157
 
6.3%
е 155
 
6.2%
и 142
 
5.7%
к 117
 
4.7%
м 117
 
4.7%
в 108
 
4.3%
Other values (46) 842
33.9%
Arabic
ValueCountFrequency (%)
ا 547
14.3%
ل 354
 
9.3%
ي 310
 
8.1%
م 259
 
6.8%
ر 224
 
5.9%
و 211
 
5.5%
ة 195
 
5.1%
ب 182
 
4.8%
ق 178
 
4.7%
ن 175
 
4.6%
Other values (32) 1187
31.1%
Greek
ValueCountFrequency (%)
ο 3
 
10.7%
ι 3
 
10.7%
τ 2
 
7.1%
α 2
 
7.1%
σ 2
 
7.1%
ω 2
 
7.1%
ϟ 2
 
7.1%
Ε 1
 
3.6%
Γ 1
 
3.6%
ά 1
 
3.6%
Other values (9) 9
32.1%
Hebrew
ValueCountFrequency (%)
ש 4
21.1%
ב 3
15.8%
ר 3
15.8%
ת 3
15.8%
מ 2
10.5%
ו 2
10.5%
ע 1
 
5.3%
י 1
 
5.3%
Inherited
ValueCountFrequency (%)
57
64.0%
21
 
23.6%
ً 5
 
5.6%
̈ 2
 
2.2%
͠ 2
 
2.2%
̊ 1
 
1.1%
͜ 1
 
1.1%
Hiragana
ValueCountFrequency (%)
17
48.6%
6
 
17.1%
4
 
11.4%
3
 
8.6%
2
 
5.7%
2
 
5.7%
1
 
2.9%
Devanagari
ValueCountFrequency (%)
2
66.7%
1
33.3%
Unknown
ValueCountFrequency (%)
1
50.0%
󰀄 1
50.0%
Vai
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9972407
98.5%
CJK 81504
 
0.8%
None 43497
 
0.4%
Thai 13576
 
0.1%
Misc Symbols 3993
 
< 0.1%
Arabic 3829
 
< 0.1%
Punctuation 2551
 
< 0.1%
Cyrillic 2484
 
< 0.1%
Hangul 606
 
< 0.1%
Katakana 516
 
< 0.1%
Other values (27) 1045
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1380403
 
13.8%
e 785155
 
7.9%
a 745156
 
7.5%
o 624907
 
6.3%
t 572056
 
5.7%
r 520408
 
5.2%
i 512966
 
5.1%
n 503348
 
5.0%
l 292061
 
2.9%
s 291905
 
2.9%
Other values (86) 3744042
37.5%
None
ValueCountFrequency (%)
è 7819
18.0%
ı 4664
 
10.7%
à 4207
 
9.7%
İ 2998
 
6.9%
ş 2950
 
6.8%
í 2654
 
6.1%
ü 2143
 
4.9%
1648
 
3.8%
ö 1474
 
3.4%
œ 1230
 
2.8%
Other values (301) 11710
26.9%
CJK
ValueCountFrequency (%)
2665
 
3.3%
1569
 
1.9%
1555
 
1.9%
1366
 
1.7%
1337
 
1.6%
1300
 
1.6%
1250
 
1.5%
1233
 
1.5%
1048
 
1.3%
1016
 
1.2%
Other values (1585) 67165
82.4%
Misc Symbols
ValueCountFrequency (%)
2286
57.3%
433
 
10.8%
367
 
9.2%
308
 
7.7%
98
 
2.5%
79
 
2.0%
44
 
1.1%
41
 
1.0%
38
 
1.0%
34
 
0.9%
Other values (47) 265
 
6.6%
Punctuation
ValueCountFrequency (%)
1286
50.4%
392
 
15.4%
288
 
11.3%
181
 
7.1%
145
 
5.7%
133
 
5.2%
40
 
1.6%
30
 
1.2%
24
 
0.9%
13
 
0.5%
Other values (6) 19
 
0.7%
Thai
ValueCountFrequency (%)
1154
 
8.5%
977
 
7.2%
879
 
6.5%
793
 
5.8%
581
 
4.3%
546
 
4.0%
508
 
3.7%
453
 
3.3%
440
 
3.2%
429
 
3.2%
Other values (57) 6816
50.2%
Arabic
ValueCountFrequency (%)
ا 547
14.3%
ل 354
 
9.2%
ي 310
 
8.1%
م 259
 
6.8%
ر 224
 
5.9%
و 211
 
5.5%
ة 195
 
5.1%
ب 182
 
4.8%
ق 178
 
4.6%
ن 175
 
4.6%
Other values (34) 1194
31.2%
Cyrillic
ValueCountFrequency (%)
а 290
 
11.7%
т 210
 
8.5%
о 186
 
7.5%
р 160
 
6.4%
н 157
 
6.3%
е 155
 
6.2%
и 142
 
5.7%
к 117
 
4.7%
м 117
 
4.7%
в 108
 
4.3%
Other values (46) 842
33.9%
Dingbats
ValueCountFrequency (%)
77
17.1%
44
 
9.8%
36
 
8.0%
34
 
7.5%
25
 
5.5%
20
 
4.4%
18
 
4.0%
18
 
4.0%
16
 
3.5%
14
 
3.1%
Other values (27) 149
33.0%
VS
ValueCountFrequency (%)
57
100.0%
Katakana
ValueCountFrequency (%)
45
 
8.7%
44
 
8.5%
29
 
5.6%
29
 
5.6%
23
 
4.5%
22
 
4.3%
20
 
3.9%
18
 
3.5%
15
 
2.9%
15
 
2.9%
Other values (56) 256
49.6%
Currency Symbols
ValueCountFrequency (%)
38
92.7%
3
 
7.3%
Box Drawing
ValueCountFrequency (%)
30
75.0%
8
 
20.0%
2
 
5.0%
Hangul
ValueCountFrequency (%)
26
 
4.3%
23
 
3.8%
20
 
3.3%
18
 
3.0%
17
 
2.8%
15
 
2.5%
15
 
2.5%
14
 
2.3%
13
 
2.1%
12
 
2.0%
Other values (170) 433
71.5%
CJK Compat Forms
ValueCountFrequency (%)
22
100.0%
Geometric Shapes
ValueCountFrequency (%)
20
29.0%
11
15.9%
8
 
11.6%
5
 
7.2%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
3
 
4.3%
2
 
2.9%
Other values (4) 6
 
8.7%
Arrows
ValueCountFrequency (%)
19
90.5%
1
 
4.8%
1
 
4.8%
CJK Compat
ValueCountFrequency (%)
19
100.0%
Emoticons
ValueCountFrequency (%)
😊 18
37.5%
😎 6
 
12.5%
😃 3
 
6.2%
😉 3
 
6.2%
😷 2
 
4.2%
😌 2
 
4.2%
🙂 2
 
4.2%
🙋 2
 
4.2%
😇 2
 
4.2%
🙌 2
 
4.2%
Other values (6) 6
 
12.5%
Hiragana
ValueCountFrequency (%)
17
48.6%
6
 
17.1%
4
 
11.4%
3
 
8.6%
2
 
5.7%
2
 
5.7%
1
 
2.9%
Math Operators
ValueCountFrequency (%)
14
25.5%
11
20.0%
10
18.2%
7
12.7%
5
 
9.1%
3
 
5.5%
2
 
3.6%
2
 
3.6%
1
 
1.8%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🅿 10
18.5%
🇲 10
18.5%
🇽 7
13.0%
🇷 6
11.1%
🇨 4
 
7.4%
🇦 3
 
5.6%
🇺 3
 
5.6%
🇫 3
 
5.6%
🇵 2
 
3.7%
🇧 1
 
1.9%
Other values (5) 5
9.3%
Block Elements
ValueCountFrequency (%)
8
57.1%
6
42.9%
Enclosed Alphanum
ValueCountFrequency (%)
7
13.2%
7
13.2%
6
11.3%
5
9.4%
5
9.4%
3
 
5.7%
3
 
5.7%
3
 
5.7%
2
 
3.8%
2
 
3.8%
Other values (10) 10
18.9%
Hebrew
ValueCountFrequency (%)
ש 4
21.1%
ב 3
15.8%
ר 3
15.8%
ת 3
15.8%
מ 2
10.5%
ו 2
10.5%
ע 1
 
5.3%
י 1
 
5.3%
IPA Ext
ValueCountFrequency (%)
ə 3
75.0%
ʖ 1
 
25.0%
Specials
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
CJK Ext A
ValueCountFrequency (%)
2
66.7%
1
33.3%
Modifier Letters
ValueCountFrequency (%)
˙ 2
50.0%
˚ 2
50.0%
Vai
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
Diacriticals
ValueCountFrequency (%)
̈ 2
33.3%
͠ 2
33.3%
̊ 1
16.7%
͜ 1
16.7%
Devanagari
ValueCountFrequency (%)
2
66.7%
1
33.3%
Latin Ext Additional
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
ế 1
14.3%
1
14.3%
1
14.3%
PUA
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct182024
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0816577 × 108
Minimum1822
Maximum3.9018744 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:13.697334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1822
5-th percentile2350364.5
Q117206558
median58269114
Q31.8328532 × 108
95-th percentile3.3745045 × 108
Maximum3.9018744 × 108
Range3.9018562 × 108
Interquartile range (IQR)1.6607876 × 108

Descriptive statistics

Standard deviation1.1085699 × 108
Coefficient of variation (CV)1.0248805
Kurtosis-0.36243009
Mean1.0816577 × 108
Median Absolute Deviation (MAD)51160362
Skewness0.95538943
Sum3.0255265 × 1013
Variance1.2289273 × 1016
MonotonicityNot monotonic
2023-10-14T06:19:13.759606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291007369 627
 
0.2%
175128252 396
 
0.1%
7518056 378
 
0.1%
97240131 339
 
0.1%
371026651 295
 
0.1%
4584648 282
 
0.1%
107434423 255
 
0.1%
6053288 241
 
0.1%
2667370 236
 
0.1%
33889201 215
 
0.1%
Other values (182014) 276448
98.8%
ValueCountFrequency (%)
1822 1
 
< 0.1%
1944 11
< 0.1%
2330 3
 
< 0.1%
2353 1
 
< 0.1%
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2626 2
 
< 0.1%
2782 3
 
< 0.1%
2787 8
< 0.1%
2827 1
 
< 0.1%
ValueCountFrequency (%)
390187445 1
< 0.1%
390178153 1
< 0.1%
390125111 1
< 0.1%
389953017 2
< 0.1%
389764135 1
< 0.1%
389750985 1
< 0.1%
389746841 1
< 0.1%
389724963 1
< 0.1%
389720737 1
< 0.1%
389716512 1
< 0.1%
Distinct4240
Distinct (%)1.5%
Missing165
Missing (%)0.1%
Memory size2.1 MiB
Minimum2008-08-12 00:00:00
Maximum2021-02-26 00:00:00
2023-10-14T06:19:13.822612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:13.886675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7159
Distinct (%)2.6%
Missing840
Missing (%)0.3%
Memory size22.1 MiB
2023-10-14T06:19:14.028176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length258
Median length191
Mean length24.890538
Min length1

Characters and Unicode

Total characters6941274
Distinct characters169
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3963 ?
Unique (%)1.4%

Sample

1st rowParis, Ile-de-France, France
2nd rowParis, Ile-de-France, France
3rd rowParis, Ile-de-France, France
4th rowParis, Ile-de-France, France
5th rowParis, Ile-de-France, France
ValueCountFrequency (%)
new 79040
 
7.5%
france 53299
 
5.1%
york 53118
 
5.1%
ile-de-france 49675
 
4.7%
paris 47932
 
4.6%
mexico 41542
 
4.0%
south 40457
 
3.9%
de 37431
 
3.6%
rio 36580
 
3.5%
united 35230
 
3.4%
Other values (7429) 573065
54.7%
2023-10-14T06:19:14.253742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
768552
 
11.1%
e 668306
 
9.6%
a 563582
 
8.1%
, 424360
 
6.1%
r 379337
 
5.5%
i 368212
 
5.3%
o 356512
 
5.1%
n 336232
 
4.8%
t 318845
 
4.6%
l 223218
 
3.2%
Other values (159) 2534118
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4532612
65.3%
Uppercase Letter 1099481
 
15.8%
Space Separator 768574
 
11.1%
Other Punctuation 426588
 
6.1%
Dash Punctuation 106295
 
1.5%
Modifier Symbol 2726
 
< 0.1%
Decimal Number 1352
 
< 0.1%
Other Number 1035
 
< 0.1%
Other Symbol 601
 
< 0.1%
Currency Symbol 465
 
< 0.1%
Other values (9) 1545
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 668306
14.7%
a 563582
12.4%
r 379337
8.4%
i 368212
 
8.1%
o 356512
 
7.9%
n 336232
 
7.4%
t 318845
 
7.0%
l 223218
 
4.9%
s 201847
 
4.5%
c 174859
 
3.9%
Other values (35) 941662
20.8%
Uppercase Letter
ValueCountFrequency (%)
F 117432
 
10.7%
S 113473
 
10.3%
I 105405
 
9.6%
N 84447
 
7.7%
R 79476
 
7.2%
C 64074
 
5.8%
T 60495
 
5.5%
A 55888
 
5.1%
P 55361
 
5.0%
Y 53536
 
4.9%
Other values (31) 309894
28.2%
Other Punctuation
ValueCountFrequency (%)
, 424360
99.5%
' 613
 
0.1%
/ 383
 
0.1%
. 321
 
0.1%
244
 
0.1%
& 116
 
< 0.1%
115
 
< 0.1%
§ 80
 
< 0.1%
70
 
< 0.1%
¡ 69
 
< 0.1%
Other values (12) 217
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 255
18.9%
2 242
17.9%
0 240
17.8%
4 169
12.5%
5 134
9.9%
9 83
 
6.1%
6 80
 
5.9%
3 76
 
5.6%
8 38
 
2.8%
7 35
 
2.6%
Other Number
ValueCountFrequency (%)
¼ 607
58.6%
¹ 257
24.8%
² 86
 
8.3%
¾ 43
 
4.2%
½ 29
 
2.8%
³ 13
 
1.3%
Modifier Symbol
ValueCountFrequency (%)
¸ 1514
55.5%
´ 867
31.8%
¨ 226
 
8.3%
¯ 111
 
4.1%
˜ 8
 
0.3%
Other Symbol
ValueCountFrequency (%)
238
39.6%
° 164
27.3%
¦ 164
27.3%
® 31
 
5.2%
© 4
 
0.7%
Currency Symbol
ValueCountFrequency (%)
£ 138
29.7%
114
24.5%
¥ 97
20.9%
¤ 69
14.8%
¢ 47
 
10.1%
Control
ValueCountFrequency (%)
 109
48.2%
 51
22.6%
 24
 
10.6%
 22
 
9.7%
 20
 
8.8%
Math Symbol
ValueCountFrequency (%)
± 321
96.1%
× 7
 
2.1%
¬ 4
 
1.2%
+ 2
 
0.6%
Initial Punctuation
ValueCountFrequency (%)
« 73
50.0%
50
34.2%
20
 
13.7%
3
 
2.1%
Final Punctuation
ValueCountFrequency (%)
45
38.5%
29
24.8%
29
24.8%
» 14
 
12.0%
Dash Punctuation
ValueCountFrequency (%)
- 106186
99.9%
103
 
0.1%
6
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
77
50.0%
42
27.3%
( 35
22.7%
Space Separator
ValueCountFrequency (%)
768552
> 99.9%
  22
 
< 0.1%
Other Letter
ValueCountFrequency (%)
ª 48
64.0%
º 27
36.0%
Format
ValueCountFrequency (%)
­ 403
100.0%
Modifier Letter
ValueCountFrequency (%)
ˆ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5632140
81.1%
Common 1309134
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 668306
 
11.9%
a 563582
 
10.0%
r 379337
 
6.7%
i 368212
 
6.5%
o 356512
 
6.3%
n 336232
 
6.0%
t 318845
 
5.7%
l 223218
 
4.0%
s 201847
 
3.6%
c 174859
 
3.1%
Other values (77) 2041190
36.2%
Common
ValueCountFrequency (%)
768552
58.7%
, 424360
32.4%
- 106186
 
8.1%
¸ 1514
 
0.1%
´ 867
 
0.1%
' 613
 
< 0.1%
¼ 607
 
< 0.1%
­ 403
 
< 0.1%
/ 383
 
< 0.1%
. 321
 
< 0.1%
Other values (72) 5328
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6924267
99.8%
None 15922
 
0.2%
Punctuation 675
 
< 0.1%
Letterlike Symbols 238
 
< 0.1%
Currency Symbols 114
 
< 0.1%
Modifier Letters 58
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
768552
 
11.1%
e 668306
 
9.7%
a 563582
 
8.1%
, 424360
 
6.1%
r 379337
 
5.5%
i 368212
 
5.3%
o 356512
 
5.1%
n 336232
 
4.9%
t 318845
 
4.6%
l 223218
 
3.2%
Other values (69) 2517111
36.4%
None
ValueCountFrequency (%)
à 2379
14.9%
Ÿ 1694
10.6%
à 1557
9.8%
¸ 1514
9.5%
Å 1416
 
8.9%
Ä 1197
 
7.5%
´ 867
 
5.4%
¼ 607
 
3.8%
ž 524
 
3.3%
­ 403
 
2.5%
Other values (61) 3764
23.6%
Letterlike Symbols
ValueCountFrequency (%)
238
100.0%
Punctuation
ValueCountFrequency (%)
115
17.0%
103
15.3%
77
11.4%
70
10.4%
59
8.7%
50
7.4%
45
 
6.7%
42
 
6.2%
29
 
4.3%
29
 
4.3%
Other values (5) 56
8.3%
Currency Symbols
ValueCountFrequency (%)
114
100.0%
Modifier Letters
ValueCountFrequency (%)
ˆ 50
86.2%
˜ 8
 
13.8%

host_response_time
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing128782
Missing (%)46.0%
Memory size14.3 MiB
within an hour
83464 
within a few hours
28891 
within a day
23425 
a few days or more
15150 

Length

Max length18
Median length14
Mean length14.856781
Min length12

Characters and Unicode

Total characters2242334
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin a day
3rd rowwithin a day
4th rowwithin an hour
5th rowwithin a few hours

Common Values

ValueCountFrequency (%)
within an hour 83464
29.8%
within a few hours 28891
 
10.3%
within a day 23425
 
8.4%
a few days or more 15150
 
5.4%
(Missing) 128782
46.0%

Length

2023-10-14T06:19:14.330542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T06:19:14.383226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
within 135780
26.5%
an 83464
16.3%
hour 83464
16.3%
a 67466
13.2%
few 44041
 
8.6%
hours 28891
 
5.6%
day 23425
 
4.6%
days 15150
 
3.0%
or 15150
 
3.0%
more 15150
 
3.0%

Most occurring characters

ValueCountFrequency (%)
361051
16.1%
i 271560
12.1%
h 248135
11.1%
n 219244
9.8%
a 189505
8.5%
w 179821
8.0%
o 142655
 
6.4%
r 142655
 
6.4%
t 135780
 
6.1%
u 112355
 
5.0%
Other values (6) 239573
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1881283
83.9%
Space Separator 361051
 
16.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 271560
14.4%
h 248135
13.2%
n 219244
11.7%
a 189505
10.1%
w 179821
9.6%
o 142655
7.6%
r 142655
7.6%
t 135780
7.2%
u 112355
6.0%
e 59191
 
3.1%
Other values (5) 180382
9.6%
Space Separator
ValueCountFrequency (%)
361051
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1881283
83.9%
Common 361051
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 271560
14.4%
h 248135
13.2%
n 219244
11.7%
a 189505
10.1%
w 179821
9.6%
o 142655
7.6%
r 142655
7.6%
t 135780
7.2%
u 112355
6.0%
e 59191
 
3.1%
Other values (5) 180382
9.6%
Common
ValueCountFrequency (%)
361051
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2242334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
361051
16.1%
i 271560
12.1%
h 248135
11.1%
n 219244
9.8%
a 189505
8.5%
w 179821
8.0%
o 142655
 
6.4%
r 142655
 
6.4%
t 135780
 
6.1%
u 112355
 
5.0%
Other values (6) 239573
10.7%

host_response_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct91
Distinct (%)0.1%
Missing128782
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean0.86593865
Minimum0
Maximum1
Zeros10640
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:14.442008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.9
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.28374432
Coefficient of variation (CV)0.32767254
Kurtosis3.8326885
Mean0.86593865
Median Absolute Deviation (MAD)0
Skewness-2.2684885
Sum130696.12
Variance0.080510838
MonotonicityNot monotonic
2023-10-14T06:19:14.502801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99334
35.5%
0 10640
 
3.8%
0.9 4982
 
1.8%
0.5 3639
 
1.3%
0.8 3085
 
1.1%
0.99 2294
 
0.8%
0.67 1901
 
0.7%
0.98 1708
 
0.6%
0.95 1633
 
0.6%
0.97 1471
 
0.5%
Other values (81) 20243
 
7.2%
(Missing) 128782
46.0%
ValueCountFrequency (%)
0 10640
3.8%
0.01 1
 
< 0.1%
0.03 4
 
< 0.1%
0.04 5
 
< 0.1%
0.05 4
 
< 0.1%
0.06 5
 
< 0.1%
0.07 5
 
< 0.1%
0.08 20
 
< 0.1%
0.09 6
 
< 0.1%
0.1 330
 
0.1%
ValueCountFrequency (%)
1 99334
35.5%
0.99 2294
 
0.8%
0.98 1708
 
0.6%
0.97 1471
 
0.5%
0.96 1207
 
0.4%
0.95 1633
 
0.6%
0.94 1289
 
0.5%
0.93 1263
 
0.5%
0.92 1015
 
0.4%
0.91 559
 
0.2%

host_acceptance_rate
Real number (ℝ)

MISSING  ZEROS 

Distinct101
Distinct (%)0.1%
Missing113087
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean0.82716843
Minimum0
Maximum1
Zeros11431
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:14.562038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.78
median0.98
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.28920249
Coefficient of variation (CV)0.34962951
Kurtosis2.3543611
Mean0.82716843
Median Absolute Deviation (MAD)0.02
Skewness-1.8638254
Sum137826.94
Variance0.083638083
MonotonicityNot monotonic
2023-10-14T06:19:14.621072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 75555
27.0%
0 11431
 
4.1%
0.99 7227
 
2.6%
0.5 5445
 
1.9%
0.98 5085
 
1.8%
0.97 4523
 
1.6%
0.67 3702
 
1.3%
0.94 3642
 
1.3%
0.96 3287
 
1.2%
0.75 3009
 
1.1%
Other values (91) 43719
 
15.6%
(Missing) 113087
40.4%
ValueCountFrequency (%)
0 11431
4.1%
0.01 25
 
< 0.1%
0.02 14
 
< 0.1%
0.03 34
 
< 0.1%
0.04 49
 
< 0.1%
0.05 28
 
< 0.1%
0.06 32
 
< 0.1%
0.07 66
 
< 0.1%
0.08 36
 
< 0.1%
0.09 37
 
< 0.1%
ValueCountFrequency (%)
1 75555
27.0%
0.99 7227
 
2.6%
0.98 5085
 
1.8%
0.97 4523
 
1.6%
0.96 3287
 
1.2%
0.95 2419
 
0.9%
0.94 3642
 
1.3%
0.93 2532
 
0.9%
0.92 2333
 
0.8%
0.91 1806
 
0.6%
Distinct2
Distinct (%)< 0.1%
Missing165
Missing (%)0.1%
Memory size546.4 KiB
False
229294 
True
50253 
(Missing)
 
165
ValueCountFrequency (%)
False 229294
82.0%
True 50253
 
18.0%
(Missing) 165
 
0.1%
2023-10-14T06:19:14.666656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

host_total_listings_count
Real number (ℝ)

SKEWED  ZEROS 

Distinct206
Distinct (%)0.1%
Missing165
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean24.581612
Minimum0
Maximum7235
Zeros33265
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:14.712837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile43
Maximum7235
Range7235
Interquartile range (IQR)3

Descriptive statistics

Standard deviation284.04114
Coefficient of variation (CV)11.555025
Kurtosis586.30572
Mean24.581612
Median Absolute Deviation (MAD)1
Skewness23.490094
Sum6871716
Variance80679.371
MonotonicityNot monotonic
2023-10-14T06:19:14.772483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 117559
42.0%
2 35778
 
12.8%
0 33265
 
11.9%
3 17230
 
6.2%
4 11216
 
4.0%
5 7993
 
2.9%
6 5880
 
2.1%
7 4666
 
1.7%
8 3895
 
1.4%
9 2978
 
1.1%
Other values (196) 39087
 
14.0%
ValueCountFrequency (%)
0 33265
 
11.9%
1 117559
42.0%
2 35778
 
12.8%
3 17230
 
6.2%
4 11216
 
4.0%
5 7993
 
2.9%
6 5880
 
2.1%
7 4666
 
1.7%
8 3895
 
1.4%
9 2978
 
1.1%
ValueCountFrequency (%)
7235 349
0.1%
7218 43
 
< 0.1%
7211 4
 
< 0.1%
2739 13
 
< 0.1%
1827 5
 
< 0.1%
1813 66
 
< 0.1%
1515 6
 
< 0.1%
1507 13
 
< 0.1%
1449 7
 
< 0.1%
1360 1
 
< 0.1%

host_has_profile_pic
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing165
Missing (%)0.1%
Memory size546.4 KiB
True
278631 
False
 
916
(Missing)
 
165
ValueCountFrequency (%)
True 278631
99.6%
False 916
 
0.3%
(Missing) 165
 
0.1%
2023-10-14T06:19:14.819202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing165
Missing (%)0.1%
Memory size546.4 KiB
True
201191 
False
78356 
(Missing)
 
165
ValueCountFrequency (%)
True 201191
71.9%
False 78356
 
28.0%
(Missing) 165
 
0.1%
2023-10-14T06:19:14.854220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct660
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.1 MiB
2023-10-14T06:19:14.988156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length22
Mean length10.677944
Min length3

Characters and Unicode

Total characters2986749
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowButtes-Montmartre
2nd rowButtes-Montmartre
3rd rowElysee
4th rowVaugirard
5th rowPassy
ValueCountFrequency (%)
ward 19086
 
4.5%
centro 15647
 
3.7%
i 14874
 
3.5%
storico 14874
 
3.5%
sydney 9262
 
2.2%
copacabana 7712
 
1.8%
cuauhtemoc 7626
 
1.8%
buttes-montmartre 7237
 
1.7%
beyoglu 6674
 
1.6%
popincourt 6206
 
1.5%
Other values (785) 311025
74.0%
2023-10-14T06:19:15.208077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 299742
 
10.0%
e 232597
 
7.8%
o 211934
 
7.1%
t 190719
 
6.4%
n 186991
 
6.3%
r 186835
 
6.3%
i 163423
 
5.5%
140511
 
4.7%
l 118846
 
4.0%
u 110759
 
3.7%
Other values (60) 1144392
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2306174
77.2%
Uppercase Letter 453954
 
15.2%
Space Separator 140511
 
4.7%
Decimal Number 42121
 
1.4%
Dash Punctuation 33951
 
1.1%
Other Punctuation 9752
 
0.3%
Open Punctuation 143
 
< 0.1%
Close Punctuation 143
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 299742
13.0%
e 232597
10.1%
o 211934
9.2%
t 190719
 
8.3%
n 186991
 
8.1%
r 186835
 
8.1%
i 163423
 
7.1%
l 118846
 
5.2%
u 110759
 
4.8%
s 92442
 
4.0%
Other values (16) 511886
22.2%
Uppercase Letter
ValueCountFrequency (%)
C 52253
11.5%
S 50157
11.0%
B 49706
10.9%
I 41192
 
9.1%
W 36867
 
8.1%
M 32467
 
7.2%
P 25179
 
5.5%
V 19928
 
4.4%
H 16904
 
3.7%
T 16797
 
3.7%
Other values (16) 112504
24.8%
Decimal Number
ValueCountFrequency (%)
1 10661
25.3%
5 9094
21.6%
7 5290
12.6%
4 5226
12.4%
6 3172
 
7.5%
2 2292
 
5.4%
3 2287
 
5.4%
0 1843
 
4.4%
8 1167
 
2.8%
9 1089
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 6820
69.9%
' 1510
 
15.5%
& 930
 
9.5%
. 492
 
5.0%
Space Separator
ValueCountFrequency (%)
140511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33951
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2760128
92.4%
Common 226621
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 299742
 
10.9%
e 232597
 
8.4%
o 211934
 
7.7%
t 190719
 
6.9%
n 186991
 
6.8%
r 186835
 
6.8%
i 163423
 
5.9%
l 118846
 
4.3%
u 110759
 
4.0%
s 92442
 
3.3%
Other values (42) 965840
35.0%
Common
ValueCountFrequency (%)
140511
62.0%
- 33951
 
15.0%
1 10661
 
4.7%
5 9094
 
4.0%
/ 6820
 
3.0%
7 5290
 
2.3%
4 5226
 
2.3%
6 3172
 
1.4%
2 2292
 
1.0%
3 2287
 
1.0%
Other values (8) 7317
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2986749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 299742
 
10.0%
e 232597
 
7.8%
o 211934
 
7.1%
t 190719
 
6.4%
n 186991
 
6.3%
r 186835
 
6.3%
i 163423
 
5.5%
140511
 
4.7%
l 118846
 
4.0%
u 110759
 
3.7%
Other values (60) 1144392
38.3%

district
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing242700
Missing (%)86.8%
Memory size9.7 MiB
Manhattan
16553 
Brooklyn
14474 
Queens
4704 
Bronx
 
992
Staten Island
 
289

Length

Max length13
Median length9
Mean length8.1516805
Min length5

Characters and Unicode

Total characters301710
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManhattan
2nd rowManhattan
3rd rowBrooklyn
4th rowManhattan
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 16553
 
5.9%
Brooklyn 14474
 
5.2%
Queens 4704
 
1.7%
Bronx 992
 
0.4%
Staten Island 289
 
0.1%
(Missing) 242700
86.8%

Length

2023-10-14T06:19:15.290609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T06:19:15.340335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 16553
44.4%
brooklyn 14474
38.8%
queens 4704
 
12.6%
bronx 992
 
2.7%
staten 289
 
0.8%
island 289
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 53854
17.8%
a 50237
16.7%
t 33684
11.2%
o 29940
9.9%
M 16553
 
5.5%
h 16553
 
5.5%
B 15466
 
5.1%
r 15466
 
5.1%
l 14763
 
4.9%
y 14474
 
4.8%
Other values (10) 40720
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 264120
87.5%
Uppercase Letter 37301
 
12.4%
Space Separator 289
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 53854
20.4%
a 50237
19.0%
t 33684
12.8%
o 29940
11.3%
h 16553
 
6.3%
r 15466
 
5.9%
l 14763
 
5.6%
y 14474
 
5.5%
k 14474
 
5.5%
e 9697
 
3.7%
Other values (4) 10978
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
M 16553
44.4%
B 15466
41.5%
Q 4704
 
12.6%
S 289
 
0.8%
I 289
 
0.8%
Space Separator
ValueCountFrequency (%)
289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301421
99.9%
Common 289
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 53854
17.9%
a 50237
16.7%
t 33684
11.2%
o 29940
9.9%
M 16553
 
5.5%
h 16553
 
5.5%
B 15466
 
5.1%
r 15466
 
5.1%
l 14763
 
4.9%
y 14474
 
4.8%
Other values (9) 40431
13.4%
Common
ValueCountFrequency (%)
289
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 53854
17.8%
a 50237
16.7%
t 33684
11.2%
o 29940
9.9%
M 16553
 
5.5%
h 16553
 
5.5%
B 15466
 
5.1%
r 15466
 
5.1%
l 14763
 
4.9%
y 14474
 
4.8%
Other values (10) 40720
13.5%

city
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
Paris
64690 
New York
37012 
Sydney
33630 
Rome
27647 
Rio de Janeiro
26615 
Other values (5)
90118 

Length

Max length14
Median length9
Mean length7.4808196
Min length4

Characters and Unicode

Total characters2092475
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParis
2nd rowParis
3rd rowParis
4th rowParis
5th rowParis

Common Values

ValueCountFrequency (%)
Paris 64690
23.1%
New York 37012
13.2%
Sydney 33630
12.0%
Rome 27647
9.9%
Rio de Janeiro 26615
9.5%
Istanbul 24519
 
8.8%
Mexico City 20065
 
7.2%
Bangkok 19361
 
6.9%
Cape Town 19086
 
6.8%
Hong Kong 7087
 
2.5%

Length

2023-10-14T06:19:15.392434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T06:19:15.444114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
paris 64690
15.5%
new 37012
8.9%
york 37012
8.9%
sydney 33630
 
8.1%
rome 27647
 
6.6%
rio 26615
 
6.4%
de 26615
 
6.4%
janeiro 26615
 
6.4%
istanbul 24519
 
5.9%
mexico 20065
 
4.8%
Other values (6) 91772
22.1%

Most occurring characters

ValueCountFrequency (%)
e 190670
 
9.1%
o 190575
 
9.1%
i 158050
 
7.6%
a 154271
 
7.4%
n 137385
 
6.6%
136480
 
6.5%
r 128317
 
6.1%
s 89209
 
4.3%
y 87325
 
4.2%
k 75734
 
3.6%
Other values (24) 744459
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1566418
74.9%
Uppercase Letter 389577
 
18.6%
Space Separator 136480
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 190670
12.2%
o 190575
12.2%
i 158050
10.1%
a 154271
9.8%
n 137385
8.8%
r 128317
8.2%
s 89209
 
5.7%
y 87325
 
5.6%
k 75734
 
4.8%
d 60245
 
3.8%
Other values (10) 294637
18.8%
Uppercase Letter
ValueCountFrequency (%)
P 64690
16.6%
R 54262
13.9%
C 39151
10.0%
Y 37012
9.5%
N 37012
9.5%
S 33630
8.6%
J 26615
6.8%
I 24519
 
6.3%
M 20065
 
5.2%
B 19361
 
5.0%
Other values (3) 33260
8.5%
Space Separator
ValueCountFrequency (%)
136480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1955995
93.5%
Common 136480
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 190670
 
9.7%
o 190575
 
9.7%
i 158050
 
8.1%
a 154271
 
7.9%
n 137385
 
7.0%
r 128317
 
6.6%
s 89209
 
4.6%
y 87325
 
4.5%
k 75734
 
3.9%
P 64690
 
3.3%
Other values (23) 679769
34.8%
Common
ValueCountFrequency (%)
136480
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2092475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 190670
 
9.1%
o 190575
 
9.1%
i 158050
 
7.6%
a 154271
 
7.4%
n 137385
 
6.6%
136480
 
6.5%
r 128317
 
6.1%
s 89209
 
4.3%
y 87325
 
4.2%
k 75734
 
3.6%
Other values (24) 744459
35.6%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct103503
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.761862
Minimum-34.2644
Maximum48.90491
Zeros0
Zeros (%)0.0%
Negative79331
Negative (%)28.4%
Memory size2.1 MiB
2023-10-14T06:19:15.509073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-34.2644
5-th percentile-33.92723
Q1-22.96439
median40.710785
Q341.90861
95-th percentile48.881385
Maximum48.90491
Range83.16931
Interquartile range (IQR)64.873

Descriptive statistics

Standard deviation32.560343
Coefficient of variation (CV)1.7354537
Kurtosis-1.2130863
Mean18.761862
Median Absolute Deviation (MAD)8.17056
Skewness-0.71093045
Sum5247917.9
Variance1060.176
MonotonicityNot monotonic
2023-10-14T06:19:15.562177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.74361 61
 
< 0.1%
48.85663 42
 
< 0.1%
22.31681 38
 
< 0.1%
40.76411 38
 
< 0.1%
48.8793 33
 
< 0.1%
48.86511 33
 
< 0.1%
48.86585 33
 
< 0.1%
48.87887 31
 
< 0.1%
41.03161 30
 
< 0.1%
48.8639 30
 
< 0.1%
Other values (103493) 279343
99.9%
ValueCountFrequency (%)
-34.2644 1
< 0.1%
-34.25267 1
< 0.1%
-34.24686 1
< 0.1%
-34.24438 1
< 0.1%
-34.2403 1
< 0.1%
-34.2395 1
< 0.1%
-34.23866 1
< 0.1%
-34.23729 1
< 0.1%
-34.2224 1
< 0.1%
-34.22208 1
< 0.1%
ValueCountFrequency (%)
48.90491 1
< 0.1%
48.90472 1
< 0.1%
48.90425 1
< 0.1%
48.90422 1
< 0.1%
48.9035 1
< 0.1%
48.90344 1
< 0.1%
48.90297 1
< 0.1%
48.90291 1
< 0.1%
48.90233 1
< 0.1%
48.90216 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct118021
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.595075
Minimum-99.33963
Maximum151.33981
Zeros0
Zeros (%)0.0%
Negative83692
Negative (%)29.9%
Memory size2.1 MiB
2023-10-14T06:19:15.617551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-99.33963
5-th percentile-99.15688
Q1-43.19804
median2.38278
Q328.98673
95-th percentile151.22784
Maximum151.33981
Range250.67944
Interquartile range (IQR)72.18477

Descriptive statistics

Standard deviation73.081309
Coefficient of variation (CV)5.8023717
Kurtosis-0.51914402
Mean12.595075
Median Absolute Deviation (MAD)45.56295
Skewness0.49055343
Sum3522993.7
Variance5340.8778
MonotonicityNot monotonic
2023-10-14T06:19:15.760756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.97249 68
 
< 0.1%
114.16968 42
 
< 0.1%
-73.99371 39
 
< 0.1%
-73.98611 33
 
< 0.1%
2.28526 28
 
< 0.1%
114.16859 27
 
< 0.1%
151.20961 25
 
< 0.1%
28.9773 25
 
< 0.1%
2.32572 25
 
< 0.1%
2.37888 25
 
< 0.1%
Other values (118011) 279375
99.9%
ValueCountFrequency (%)
-99.33963 1
< 0.1%
-99.33931 1
< 0.1%
-99.33869 1
< 0.1%
-99.33621 1
< 0.1%
-99.32671 1
< 0.1%
-99.32409 1
< 0.1%
-99.32114 1
< 0.1%
-99.31947 1
< 0.1%
-99.3179 1
< 0.1%
-99.31142 1
< 0.1%
ValueCountFrequency (%)
151.33981 1
< 0.1%
151.33977 1
< 0.1%
151.33973 1
< 0.1%
151.33923 1
< 0.1%
151.3392 1
< 0.1%
151.339 1
< 0.1%
151.33899 1
< 0.1%
151.33898 1
< 0.1%
151.33894 1
< 0.1%
151.33878 1
< 0.1%
Distinct144
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
2023-10-14T06:19:15.839481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length16
Mean length18.755227
Min length3

Characters and Unicode

Total characters5246062
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowEntire apartment
2nd rowEntire apartment
3rd rowEntire apartment
4th rowEntire apartment
5th rowEntire apartment
ValueCountFrequency (%)
apartment 195172
25.4%
entire 180607
23.5%
room 98419
12.8%
in 97949
12.7%
private 80560
10.5%
house 27798
 
3.6%
condominium 15911
 
2.1%
hotel 9024
 
1.2%
serviced 6433
 
0.8%
boutique 5808
 
0.8%
Other values (60) 51834
 
6.7%
2023-10-14T06:19:15.997645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 700691
13.4%
r 560935
10.7%
e 551172
10.5%
n 517623
9.9%
a 500157
9.5%
489803
9.3%
i 409781
7.8%
m 325786
6.2%
o 295824
5.6%
p 197195
 
3.8%
Other values (32) 697095
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4476298
85.3%
Space Separator 489803
 
9.3%
Uppercase Letter 279822
 
5.3%
Other Punctuation 136
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 700691
15.7%
r 560935
12.5%
e 551172
12.3%
n 517623
11.6%
a 500157
11.2%
i 409781
9.2%
m 325786
7.3%
o 295824
6.6%
p 197195
 
4.4%
v 89116
 
2.0%
Other values (14) 328018
7.3%
Uppercase Letter
ValueCountFrequency (%)
E 180638
64.6%
P 80563
28.8%
R 13052
 
4.7%
S 4862
 
1.7%
T 308
 
0.1%
C 119
 
< 0.1%
B 81
 
< 0.1%
F 80
 
< 0.1%
V 55
 
< 0.1%
H 22
 
< 0.1%
Other values (5) 42
 
< 0.1%
Space Separator
ValueCountFrequency (%)
489803
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4756120
90.7%
Common 489942
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 700691
14.7%
r 560935
11.8%
e 551172
11.6%
n 517623
10.9%
a 500157
10.5%
i 409781
8.6%
m 325786
6.8%
o 295824
6.2%
p 197195
 
4.1%
E 180638
 
3.8%
Other values (29) 516318
10.9%
Common
ValueCountFrequency (%)
489803
> 99.9%
/ 136
 
< 0.1%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5246062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 700691
13.4%
r 560935
10.7%
e 551172
10.5%
n 517623
9.9%
a 500157
9.5%
489803
9.3%
i 409781
7.8%
m 325786
6.2%
o 295824
5.6%
p 197195
 
3.8%
Other values (32) 697095
13.3%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.4 MiB
Entire place
182005 
Private room
86988 
Hotel room
 
5857
Shared room
 
4862

Length

Max length12
Median length12
Mean length11.940739
Min length10

Characters and Unicode

Total characters3339968
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire place
2nd rowEntire place
3rd rowEntire place
4th rowEntire place
5th rowEntire place

Common Values

ValueCountFrequency (%)
Entire place 182005
65.1%
Private room 86988
31.1%
Hotel room 5857
 
2.1%
Shared room 4862
 
1.7%

Length

2023-10-14T06:19:16.071721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T06:19:16.118591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 182005
32.5%
place 182005
32.5%
room 97707
17.5%
private 86988
15.5%
hotel 5857
 
1.0%
shared 4862
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 461717
13.8%
r 371562
11.1%
279712
8.4%
t 274850
8.2%
a 273855
8.2%
i 268993
8.1%
o 201271
 
6.0%
l 187862
 
5.6%
c 182005
 
5.4%
E 182005
 
5.4%
Other values (9) 656136
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2780544
83.3%
Space Separator 279712
 
8.4%
Uppercase Letter 279712
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 461717
16.6%
r 371562
13.4%
t 274850
9.9%
a 273855
9.8%
i 268993
9.7%
o 201271
7.2%
l 187862
6.8%
c 182005
 
6.5%
n 182005
 
6.5%
p 182005
 
6.5%
Other values (4) 194419
7.0%
Uppercase Letter
ValueCountFrequency (%)
E 182005
65.1%
P 86988
31.1%
H 5857
 
2.1%
S 4862
 
1.7%
Space Separator
ValueCountFrequency (%)
279712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3060256
91.6%
Common 279712
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 461717
15.1%
r 371562
12.1%
t 274850
9.0%
a 273855
8.9%
i 268993
8.8%
o 201271
6.6%
l 187862
 
6.1%
c 182005
 
5.9%
E 182005
 
5.9%
n 182005
 
5.9%
Other values (8) 474131
15.5%
Common
ValueCountFrequency (%)
279712
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 461717
13.8%
r 371562
11.1%
279712
8.4%
t 274850
8.2%
a 273855
8.2%
i 268993
8.1%
o 201271
 
6.0%
l 187862
 
5.6%
c 182005
 
5.4%
E 182005
 
5.4%
Other values (9) 656136
19.6%

accommodates
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2887363
Minimum0
Maximum16
Zeros85
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:16.161217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1333785
Coefficient of variation (CV)0.6486925
Kurtosis7.4441247
Mean3.2887363
Median Absolute Deviation (MAD)1
Skewness2.1976089
Sum919899
Variance4.551304
MonotonicityNot monotonic
2023-10-14T06:19:16.211327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 118332
42.3%
4 57260
20.5%
3 27936
 
10.0%
1 25813
 
9.2%
6 19455
 
7.0%
5 13527
 
4.8%
8 6514
 
2.3%
7 3697
 
1.3%
10 2630
 
0.9%
16 1162
 
0.4%
Other values (7) 3386
 
1.2%
ValueCountFrequency (%)
0 85
 
< 0.1%
1 25813
 
9.2%
2 118332
42.3%
3 27936
 
10.0%
4 57260
20.5%
5 13527
 
4.8%
6 19455
 
7.0%
7 3697
 
1.3%
8 6514
 
2.3%
9 1140
 
0.4%
ValueCountFrequency (%)
16 1162
 
0.4%
15 214
 
0.1%
14 331
 
0.1%
13 197
 
0.1%
12 1052
 
0.4%
11 367
 
0.1%
10 2630
0.9%
9 1140
 
0.4%
8 6514
2.3%
7 3697
1.3%

bedrooms
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)< 0.1%
Missing29435
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean1.5155088
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:16.266156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum50
Range49
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1530795
Coefficient of variation (CV)0.76085305
Kurtosis428.89304
Mean1.5155088
Median Absolute Deviation (MAD)0
Skewness13.213384
Sum379297
Variance1.3295923
MonotonicityNot monotonic
2023-10-14T06:19:16.320380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 170163
60.8%
2 51382
 
18.4%
3 18525
 
6.6%
4 6579
 
2.4%
5 2106
 
0.8%
6 701
 
0.3%
7 246
 
0.1%
10 150
 
0.1%
8 124
 
< 0.1%
9 79
 
< 0.1%
Other values (29) 222
 
0.1%
(Missing) 29435
 
10.5%
ValueCountFrequency (%)
1 170163
60.8%
2 51382
 
18.4%
3 18525
 
6.6%
4 6579
 
2.4%
5 2106
 
0.8%
6 701
 
0.3%
7 246
 
0.1%
8 124
 
< 0.1%
9 79
 
< 0.1%
10 150
 
0.1%
ValueCountFrequency (%)
50 22
< 0.1%
48 1
 
< 0.1%
46 2
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 10
< 0.1%
39 2
 
< 0.1%
38 2
 
< 0.1%
35 2
 
< 0.1%
34 1
 
< 0.1%
Distinct245003
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size95.3 MiB
2023-10-14T06:19:16.484613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3154
Median length1007
Mean length300.21535
Min length2

Characters and Unicode

Total characters83973836
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230168 ?
Unique (%)82.3%

Sample

1st row["Heating", "Kitchen", "Washer", "Wifi", "Long term stays allowed"]
2nd row["Shampoo", "Heating", "Kitchen", "Essentials", "Washer", "Dryer", "Wifi", "Long term stays allowed"]
3rd row["Heating", "TV", "Kitchen", "Washer", "Wifi", "Long term stays allowed"]
4th row["Heating", "TV", "Kitchen", "Wifi", "Long term stays allowed"]
5th row["Heating", "TV", "Kitchen", "Essentials", "Hair dryer", "Washer", "Dryer", "Bathtub", "Wifi", "Elevator", "Long term stays allowed", "Cable TV"]
ValueCountFrequency (%)
allowed 297730
 
3.0%
dryer 283186
 
2.9%
tv 276363
 
2.8%
wifi 272203
 
2.8%
essentials 259724
 
2.6%
kitchen 242517
 
2.5%
term 241054
 
2.5%
long 241054
 
2.5%
stays 241054
 
2.5%
alarm 234957
 
2.4%
Other values (2177) 7212935
73.6%
2023-10-14T06:19:16.736357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 10941840
13.0%
9523264
 
11.3%
e 6499632
 
7.7%
, 5192388
 
6.2%
r 5072288
 
6.0%
a 5038520
 
6.0%
i 4496984
 
5.4%
o 4221380
 
5.0%
s 3595904
 
4.3%
n 3588586
 
4.3%
Other values (73) 25803050
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51651420
61.5%
Other Punctuation 16189128
 
19.3%
Space Separator 9523264
 
11.3%
Uppercase Letter 5868571
 
7.0%
Open Punctuation 279739
 
0.3%
Close Punctuation 279739
 
0.3%
Decimal Number 156820
 
0.2%
Dash Punctuation 25050
 
< 0.1%
Currency Symbol 77
 
< 0.1%
Math Symbol 28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6499632
12.6%
r 5072288
9.8%
a 5038520
9.8%
i 4496984
 
8.7%
o 4221380
 
8.2%
s 3595904
 
7.0%
n 3588586
 
6.9%
t 3320669
 
6.4%
l 2026165
 
3.9%
d 1890745
 
3.7%
Other values (16) 11900547
23.0%
Uppercase Letter
ValueCountFrequency (%)
H 833002
14.2%
S 477721
 
8.1%
W 463158
 
7.9%
D 451900
 
7.7%
E 437657
 
7.5%
C 407259
 
6.9%
L 375569
 
6.4%
F 323967
 
5.5%
T 282961
 
4.8%
V 279798
 
4.8%
Other values (16) 1535579
26.2%
Other Punctuation
ValueCountFrequency (%)
" 10941840
67.6%
, 5192388
32.1%
\ 39250
 
0.2%
/ 12812
 
0.1%
: 2449
 
< 0.1%
' 135
 
< 0.1%
& 126
 
< 0.1%
. 114
 
< 0.1%
% 7
 
< 0.1%
; 3
 
< 0.1%
Other values (3) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 44343
28.3%
1 37113
23.7%
2 36862
23.5%
9 30315
19.3%
3 6131
 
3.9%
5 809
 
0.5%
4 775
 
0.5%
6 191
 
0.1%
7 160
 
0.1%
8 121
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[ 279712
> 99.9%
( 27
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 279712
> 99.9%
) 27
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9523264
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25050
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 77
100.0%
Math Symbol
ValueCountFrequency (%)
+ 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57519991
68.5%
Common 26453845
31.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6499632
 
11.3%
r 5072288
 
8.8%
a 5038520
 
8.8%
i 4496984
 
7.8%
o 4221380
 
7.3%
s 3595904
 
6.3%
n 3588586
 
6.2%
t 3320669
 
5.8%
l 2026165
 
3.5%
d 1890745
 
3.3%
Other values (42) 17769118
30.9%
Common
ValueCountFrequency (%)
" 10941840
41.4%
9523264
36.0%
, 5192388
19.6%
[ 279712
 
1.1%
] 279712
 
1.1%
0 44343
 
0.2%
\ 39250
 
0.1%
1 37113
 
0.1%
2 36862
 
0.1%
9 30315
 
0.1%
Other values (21) 49046
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83973836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 10941840
13.0%
9523264
 
11.3%
e 6499632
 
7.7%
, 5192388
 
6.2%
r 5072288
 
6.0%
a 5038520
 
6.0%
i 4496984
 
5.4%
o 4221380
 
5.0%
s 3595904
 
4.3%
n 3588586
 
4.3%
Other values (73) 25803050
30.7%

price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5194
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.79274
Minimum0
Maximum625216
Zeros113
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:16.817746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q175
median150
Q3474
95-th percentile2000
Maximum625216
Range625216
Interquartile range (IQR)399

Descriptive statistics

Standard deviation3441.8266
Coefficient of variation (CV)5.6535277
Kurtosis7173.3277
Mean608.79274
Median Absolute Deviation (MAD)99
Skewness61.616178
Sum1.7028663 × 108
Variance11846170
MonotonicityNot monotonic
2023-10-14T06:19:16.876124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6882
 
2.5%
50 6438
 
2.3%
70 6207
 
2.2%
60 6172
 
2.2%
80 6148
 
2.2%
150 5351
 
1.9%
90 4828
 
1.7%
120 4300
 
1.5%
200 4164
 
1.5%
75 4130
 
1.5%
Other values (5184) 225092
80.5%
ValueCountFrequency (%)
0 113
< 0.1%
1 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 7
 
< 0.1%
9 13
 
< 0.1%
10 37
 
< 0.1%
11 13
 
< 0.1%
12 46
< 0.1%
13 33
 
< 0.1%
ValueCountFrequency (%)
625216 1
 
< 0.1%
499000 1
 
< 0.1%
350000 1
 
< 0.1%
300177 1
 
< 0.1%
300000 2
< 0.1%
231047 1
 
< 0.1%
206499 1
 
< 0.1%
180124 1
 
< 0.1%
180000 3
< 0.1%
179532 3
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct202
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0509667
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:16.936836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum9999
Range9998
Interquartile range (IQR)4

Descriptive statistics

Standard deviation31.518946
Coefficient of variation (CV)3.9149269
Kurtosis36317.537
Mean8.0509667
Median Absolute Deviation (MAD)1
Skewness123.17087
Sum2251952
Variance993.44399
MonotonicityNot monotonic
2023-10-14T06:19:16.996239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 92839
33.2%
2 60877
21.8%
3 37869
13.5%
30 28881
 
10.3%
5 13770
 
4.9%
4 12856
 
4.6%
7 11356
 
4.1%
6 3471
 
1.2%
10 2810
 
1.0%
14 2330
 
0.8%
Other values (192) 12653
 
4.5%
ValueCountFrequency (%)
1 92839
33.2%
2 60877
21.8%
3 37869
13.5%
4 12856
 
4.6%
5 13770
 
4.9%
6 3471
 
1.2%
7 11356
 
4.1%
8 504
 
0.2%
9 211
 
0.1%
10 2810
 
1.0%
ValueCountFrequency (%)
9999 1
 
< 0.1%
1250 1
 
< 0.1%
1125 3
 
< 0.1%
1124 3
 
< 0.1%
1123 1
 
< 0.1%
1112 1
 
< 0.1%
1100 1
 
< 0.1%
1001 1
 
< 0.1%
1000 31
< 0.1%
999 3
 
< 0.1%

maximum_nights
Real number (ℝ)

SKEWED 

Distinct508
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27558.597
Minimum1
Maximum2.1474836 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.054392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q145
median1125
Q31125
95-th percentile1125
Maximum2.1474836 × 109
Range2.1474836 × 109
Interquartile range (IQR)1080

Descriptive statistics

Standard deviation7282875.2
Coefficient of variation (CV)264.26872
Kurtosis82346.384
Mean27558.597
Median Absolute Deviation (MAD)0
Skewness284.22065
Sum7.7084702 × 109
Variance5.3040271 × 1013
MonotonicityNot monotonic
2023-10-14T06:19:17.113294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 157596
56.3%
30 20449
 
7.3%
365 15659
 
5.6%
90 8165
 
2.9%
60 6368
 
2.3%
7 5244
 
1.9%
15 5154
 
1.8%
28 4344
 
1.6%
14 4016
 
1.4%
10 4000
 
1.4%
Other values (498) 48717
 
17.4%
ValueCountFrequency (%)
1 501
 
0.2%
2 747
 
0.3%
3 1286
 
0.5%
4 1067
 
0.4%
5 1928
 
0.7%
6 1330
 
0.5%
7 5244
1.9%
8 1090
 
0.4%
9 487
 
0.2%
10 4000
1.4%
ValueCountFrequency (%)
2147483647 3
< 0.1%
999999999 1
 
< 0.1%
20000000 2
< 0.1%
10000000 3
< 0.1%
999999 1
 
< 0.1%
471000 1
 
< 0.1%
200000 1
 
< 0.1%
100000 1
 
< 0.1%
99999 3
< 0.1%
85554 1
 
< 0.1%

review_scores_rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)< 0.1%
Missing91405
Missing (%)32.7%
Infinite0
Infinite (%)0.0%
Mean93.405195
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.170657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile80
Q191
median96
Q3100
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.070437
Coefficient of variation (CV)0.10781453
Kurtosis19.998758
Mean93.405195
Median Absolute Deviation (MAD)4
Skewness-3.7584498
Sum17588852
Variance101.41371
MonotonicityNot monotonic
2023-10-14T06:19:17.233280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 57458
20.5%
98 13616
 
4.9%
97 12425
 
4.4%
96 12261
 
4.4%
93 10995
 
3.9%
95 10950
 
3.9%
90 9510
 
3.4%
80 8922
 
3.2%
99 8555
 
3.1%
94 7739
 
2.8%
Other values (53) 35876
 
12.8%
(Missing) 91405
32.7%
ValueCountFrequency (%)
20 1137
0.4%
27 1
 
< 0.1%
30 25
 
< 0.1%
31 1
 
< 0.1%
33 6
 
< 0.1%
35 3
 
< 0.1%
36 1
 
< 0.1%
40 682
0.2%
43 2
 
< 0.1%
44 1
 
< 0.1%
ValueCountFrequency (%)
100 57458
20.5%
99 8555
 
3.1%
98 13616
 
4.9%
97 12425
 
4.4%
96 12261
 
4.4%
95 10950
 
3.9%
94 7739
 
2.8%
93 10995
 
3.9%
92 5626
 
2.0%
91 4772
 
1.7%

review_scores_accuracy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91713
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.5654764
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.284403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99087789
Coefficient of variation (CV)0.10358897
Kurtosis24.054205
Mean9.5654764
Median Absolute Deviation (MAD)0
Skewness-4.2133097
Sum1798300
Variance0.981839
MonotonicityNot monotonic
2023-10-14T06:19:17.333058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 136390
48.8%
9 37176
 
13.3%
8 8712
 
3.1%
6 1974
 
0.7%
7 1795
 
0.6%
2 1152
 
0.4%
4 527
 
0.2%
5 240
 
0.1%
3 33
 
< 0.1%
(Missing) 91713
32.8%
ValueCountFrequency (%)
2 1152
 
0.4%
3 33
 
< 0.1%
4 527
 
0.2%
5 240
 
0.1%
6 1974
 
0.7%
7 1795
 
0.6%
8 8712
 
3.1%
9 37176
 
13.3%
10 136390
48.8%
ValueCountFrequency (%)
10 136390
48.8%
9 37176
 
13.3%
8 8712
 
3.1%
7 1795
 
0.6%
6 1974
 
0.7%
5 240
 
0.1%
4 527
 
0.2%
3 33
 
< 0.1%
2 1152
 
0.4%

review_scores_cleanliness
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91665
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.3128686
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.379866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1460716
Coefficient of variation (CV)0.12306322
Kurtosis13.024349
Mean9.3128686
Median Absolute Deviation (MAD)0
Skewness-3.0278585
Sum1751257
Variance1.3134802
MonotonicityNot monotonic
2023-10-14T06:19:17.426636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 109029
39.0%
9 52107
18.6%
8 17148
 
6.1%
7 3810
 
1.4%
6 3209
 
1.1%
2 1271
 
0.5%
4 853
 
0.3%
5 541
 
0.2%
3 79
 
< 0.1%
(Missing) 91665
32.8%
ValueCountFrequency (%)
2 1271
 
0.5%
3 79
 
< 0.1%
4 853
 
0.3%
5 541
 
0.2%
6 3209
 
1.1%
7 3810
 
1.4%
8 17148
 
6.1%
9 52107
18.6%
10 109029
39.0%
ValueCountFrequency (%)
10 109029
39.0%
9 52107
18.6%
8 17148
 
6.1%
7 3810
 
1.4%
6 3209
 
1.1%
5 541
 
0.2%
4 853
 
0.3%
3 79
 
< 0.1%
2 1271
 
0.5%

review_scores_checkin
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91771
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.701534
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.474089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86743384
Coefficient of variation (CV)0.089412029
Kurtosis36.579086
Mean9.701534
Median Absolute Deviation (MAD)0
Skewness-5.2621276
Sum1823316
Variance0.75244146
MonotonicityNot monotonic
2023-10-14T06:19:17.521940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 152997
54.7%
9 25292
 
9.0%
8 5685
 
2.0%
6 1381
 
0.5%
7 1135
 
0.4%
2 963
 
0.3%
4 327
 
0.1%
5 145
 
0.1%
3 16
 
< 0.1%
(Missing) 91771
32.8%
ValueCountFrequency (%)
2 963
 
0.3%
3 16
 
< 0.1%
4 327
 
0.1%
5 145
 
0.1%
6 1381
 
0.5%
7 1135
 
0.4%
8 5685
 
2.0%
9 25292
 
9.0%
10 152997
54.7%
ValueCountFrequency (%)
10 152997
54.7%
9 25292
 
9.0%
8 5685
 
2.0%
7 1135
 
0.4%
6 1381
 
0.5%
5 145
 
0.1%
4 327
 
0.1%
3 16
 
< 0.1%
2 963
 
0.3%

review_scores_communication
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91687
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.6985933
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.569622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q110
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88688374
Coefficient of variation (CV)0.091444575
Kurtosis35.555497
Mean9.6985933
Median Absolute Deviation (MAD)0
Skewness-5.2302565
Sum1823578
Variance0.78656277
MonotonicityNot monotonic
2023-10-14T06:19:17.618581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 153549
54.9%
9 24572
 
8.8%
8 5668
 
2.0%
6 1434
 
0.5%
7 1229
 
0.4%
2 1020
 
0.4%
4 372
 
0.1%
5 159
 
0.1%
3 22
 
< 0.1%
(Missing) 91687
32.8%
ValueCountFrequency (%)
2 1020
 
0.4%
3 22
 
< 0.1%
4 372
 
0.1%
5 159
 
0.1%
6 1434
 
0.5%
7 1229
 
0.4%
8 5668
 
2.0%
9 24572
 
8.8%
10 153549
54.9%
ValueCountFrequency (%)
10 153549
54.9%
9 24572
 
8.8%
8 5668
 
2.0%
7 1229
 
0.4%
6 1434
 
0.5%
5 159
 
0.1%
4 372
 
0.1%
3 22
 
< 0.1%
2 1020
 
0.4%

review_scores_location
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91775
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.6339944
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.665942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8332338
Coefficient of variation (CV)0.086488923
Kurtosis28.388746
Mean9.6339944
Median Absolute Deviation (MAD)0
Skewness-4.3179909
Sum1810584
Variance0.69427857
MonotonicityNot monotonic
2023-10-14T06:19:17.714089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 140495
50.2%
9 35350
 
12.6%
8 8465
 
3.0%
6 1480
 
0.5%
7 1151
 
0.4%
2 635
 
0.2%
4 234
 
0.1%
5 120
 
< 0.1%
3 7
 
< 0.1%
(Missing) 91775
32.8%
ValueCountFrequency (%)
2 635
 
0.2%
3 7
 
< 0.1%
4 234
 
0.1%
5 120
 
< 0.1%
6 1480
 
0.5%
7 1151
 
0.4%
8 8465
 
3.0%
9 35350
 
12.6%
10 140495
50.2%
ValueCountFrequency (%)
10 140495
50.2%
9 35350
 
12.6%
8 8465
 
3.0%
7 1151
 
0.4%
6 1480
 
0.5%
5 120
 
< 0.1%
4 234
 
0.1%
3 7
 
< 0.1%
2 635
 
0.2%

review_scores_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing91785
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean9.3353643
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-10-14T06:19:17.762126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0426247
Coefficient of variation (CV)0.11168548
Kurtosis16.304493
Mean9.3353643
Median Absolute Deviation (MAD)0
Skewness-3.2538417
Sum1754367
Variance1.0870662
MonotonicityNot monotonic
2023-10-14T06:19:17.810006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 102530
36.7%
9 63670
22.8%
8 14588
 
5.2%
6 2602
 
0.9%
7 2497
 
0.9%
2 1095
 
0.4%
4 605
 
0.2%
5 306
 
0.1%
3 34
 
< 0.1%
(Missing) 91785
32.8%
ValueCountFrequency (%)
2 1095
 
0.4%
3 34
 
< 0.1%
4 605
 
0.2%
5 306
 
0.1%
6 2602
 
0.9%
7 2497
 
0.9%
8 14588
 
5.2%
9 63670
22.8%
10 102530
36.7%
ValueCountFrequency (%)
10 102530
36.7%
9 63670
22.8%
8 14588
 
5.2%
7 2497
 
0.9%
6 2602
 
0.9%
5 306
 
0.1%
4 605
 
0.2%
3 34
 
< 0.1%
2 1095
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size273.3 KiB
False
164105 
True
115607 
ValueCountFrequency (%)
False 164105
58.7%
True 115607
41.3%
2023-10-14T06:19:17.853511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2023-10-14T06:19:09.481945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:50.916218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.108455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.167367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.176345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.142363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.177229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.205206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.184833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.302361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.267757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.272808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.339871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.318233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.340185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.413579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.403568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.480466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.482141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.532606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.008183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.165238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.215575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.226147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.196631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.225444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.255211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.238317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.355194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.318369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.322131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.389584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.370334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.390954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.464920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.453863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.530376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.534187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.584950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.090744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.220526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.267190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.277288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.251411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.275783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.308143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.292804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.406272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.372926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.374257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.440599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.424742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.445147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.517784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.507013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.583876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.586878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.715511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.149579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.274691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.313600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.324805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.301273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.321122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.357090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.344789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.454135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.421618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.425119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.488321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.474620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.493429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.567097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.555485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-10-14T06:18:57.917044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.944247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.008830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.000651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.988174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.051297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.056996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.142213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.131201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.125908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.205884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.208891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:10.342021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.872447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.943549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.973244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.928360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.953857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.997919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.970315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.998140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.060744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.055385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.123258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.105741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.112884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.196493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.184939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.263697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.260585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.265087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:10.396901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.927301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.000902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.023549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.979362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.009565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.049479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.025341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.054054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.111306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.108842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.178312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.158529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.167421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.250186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.238668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.316337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.315768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.318965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:10.450723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:51.982588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.057097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.073606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.032645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.064862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.101441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.077906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.109413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.162737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.164690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.231858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.211673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.226835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.303819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.294018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.371312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.371137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.374616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:10.504967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:52.037521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:53.113832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:54.124287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:55.084121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:56.120166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:57.152468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:58.130992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:18:59.247074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:00.214271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:01.218010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:02.285893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:03.264039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:04.282274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:05.358418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:06.348264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:07.424871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:08.426716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-14T06:19:09.427511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-14T06:19:17.983755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
listing_idhost_idhost_response_ratehost_acceptance_ratehost_total_listings_countlatitudelongitudeaccommodatesbedroomspriceminimum_nightsmaximum_nightsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valuehost_response_timehost_is_superhosthost_has_profile_pichost_identity_verifieddistrictcityroom_typeinstant_bookable
listing_id1.0000.554-0.0470.060-0.027-0.0490.015-0.018-0.0040.085-0.1560.0100.1230.0140.035-0.024-0.0220.0790.0540.0800.0780.0220.0860.0700.1030.0870.255
host_id0.5541.000-0.0570.148-0.101-0.0730.062-0.038-0.0270.094-0.2220.0090.003-0.043-0.001-0.060-0.065-0.0040.0000.0790.0770.0340.1590.1140.1000.1010.235
host_response_rate-0.047-0.0571.0000.234-0.037-0.0480.011-0.005-0.0080.0210.034-0.0440.1170.1350.1320.1310.1710.0600.1250.6320.2310.0380.1780.0410.0460.0590.072
host_acceptance_rate0.0600.1480.2341.000-0.0350.0590.029-0.005-0.0650.011-0.153-0.002-0.0090.0150.0450.0070.015-0.0090.0260.2590.1840.0180.1230.0750.0870.0560.414
host_total_listings_count-0.027-0.101-0.037-0.0351.000-0.1010.0800.0720.0170.170-0.1050.121-0.141-0.127-0.044-0.099-0.124-0.054-0.1210.0390.0310.0010.0430.0640.0570.0220.055
latitude-0.049-0.073-0.0480.059-0.1011.000-0.285-0.043-0.140-0.496-0.008-0.009-0.098-0.029-0.086-0.054-0.032-0.046-0.0740.0510.0910.0100.0920.1990.8950.0960.118
longitude0.0150.0620.0110.0290.080-0.2851.0000.0150.0450.095-0.1870.009-0.034-0.059-0.024-0.047-0.034-0.082-0.0250.0740.1180.0140.1421.0001.0000.1660.175
accommodates-0.018-0.038-0.005-0.0050.072-0.0430.0151.0000.7070.3420.0280.029-0.012-0.0080.0050.006-0.0020.021-0.0440.0330.0550.0110.0850.0490.0960.3010.031
bedrooms-0.004-0.027-0.008-0.0650.017-0.1400.0450.7071.0000.3660.1030.0000.0340.0060.0190.0250.0150.036-0.0170.0170.0070.0000.0140.0100.0230.0190.016
price0.0850.0940.0210.0110.170-0.4960.0950.3420.3661.000-0.0910.0830.1110.0400.1270.0310.0190.0970.0420.0070.0020.0050.0071.0000.0130.0080.002
minimum_nights-0.156-0.2220.034-0.153-0.105-0.008-0.1870.0280.103-0.0911.000-0.0450.0760.068-0.0110.0650.0720.0250.0340.0000.0000.0000.0010.0000.0000.0000.000
maximum_nights0.0100.009-0.044-0.0020.121-0.0090.0090.0290.0000.083-0.0451.000-0.060-0.061-0.044-0.066-0.066-0.019-0.0590.0040.0000.0000.0000.0000.0000.0000.000
review_scores_rating0.1230.0030.117-0.009-0.141-0.098-0.034-0.0120.0340.1110.076-0.0601.0000.5850.5920.4400.4720.3300.6060.0590.2700.0170.0710.0420.0500.0470.076
review_scores_accuracy0.014-0.0430.1350.015-0.127-0.029-0.059-0.0080.0060.0400.068-0.0610.5851.0000.5120.4880.5120.3310.5420.0540.2240.0160.0660.0460.0480.0530.076
review_scores_cleanliness0.035-0.0010.1320.045-0.044-0.086-0.0240.0050.0190.127-0.011-0.0440.5920.5121.0000.3760.3830.2480.5020.0620.2490.0130.0720.0370.0650.0380.035
review_scores_checkin-0.024-0.0600.1310.007-0.099-0.054-0.0470.0060.0250.0310.065-0.0660.4400.4880.3761.0000.5800.2970.4040.0540.1800.0140.0540.0440.0490.0370.068
review_scores_communication-0.022-0.0650.1710.015-0.124-0.032-0.034-0.0020.0150.0190.072-0.0660.4720.5120.3830.5801.0000.2990.4350.0590.1880.0140.0560.0370.0400.0490.084
review_scores_location0.079-0.0040.060-0.009-0.054-0.046-0.0820.0210.0360.0970.025-0.0190.3300.3310.2480.2970.2991.0000.3270.0520.1270.0100.0640.0800.0780.0500.037
review_scores_value0.0540.0000.1250.026-0.121-0.074-0.025-0.044-0.0170.0420.034-0.0590.6060.5420.5020.4040.4350.3271.0000.0530.2120.0100.0760.0520.0590.0580.057
host_response_time0.0800.0790.6320.2590.0390.0510.0740.0330.0170.0070.0000.0040.0590.0540.0620.0540.0590.0520.0531.0000.2340.0360.1830.0710.0780.0530.213
host_is_superhost0.0780.0770.2310.1840.0310.0910.1180.0550.0070.0020.0000.0000.2700.2240.2490.1800.1880.1270.2120.2341.0000.0190.1380.0880.1560.0600.036
host_has_profile_pic0.0220.0340.0380.0180.0010.0100.0140.0110.0000.0050.0000.0000.0170.0160.0130.0140.0140.0100.0100.0360.0191.0000.0480.0040.0140.0080.003
host_identity_verified0.0860.1590.1780.1230.0430.0920.1420.0850.0140.0070.0010.0000.0710.0660.0720.0540.0560.0640.0760.1830.1380.0481.0000.0640.1530.1210.006
district0.0700.1140.0410.0750.0640.1991.0000.0490.0101.0000.0000.0000.0420.0460.0370.0440.0370.0800.0520.0710.0880.0040.0641.0001.0000.1160.063
city0.1030.1000.0460.0870.0570.8951.0000.0960.0230.0130.0000.0000.0500.0480.0650.0490.0400.0780.0590.0780.1560.0140.1531.0001.0000.1840.224
room_type0.0870.1010.0590.0560.0220.0960.1660.3010.0190.0080.0000.0000.0470.0530.0380.0370.0490.0500.0580.0530.0600.0080.1210.1160.1841.0000.147
instant_bookable0.2550.2350.0720.4140.0550.1180.1750.0310.0160.0020.0000.0000.0760.0760.0350.0680.0840.0370.0570.2130.0360.0030.0060.0630.2240.1471.000

Missing values

2023-10-14T06:19:10.658878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-14T06:19:11.108669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-14T06:19:12.396724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

listing_idnamehost_idhost_sincehost_locationhost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_total_listings_counthost_has_profile_pichost_identity_verifiedneighbourhooddistrictcitylatitudelongitudeproperty_typeroom_typeaccommodatesbedroomsamenitiespriceminimum_nightsmaximum_nightsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueinstant_bookable
0281420Beautiful Flat in le Village Montmartre, Paris14669192011-12-03Paris, Ile-de-France, FranceNaNNaNNaNf1.0tfButtes-MontmartreNaNParis48.886682.33343Entire apartmentEntire place21.0["Heating", "Kitchen", "Washer", "Wifi", "Long term stays allowed"]5321125100.010.010.010.010.010.010.0f
1370518339 m² Paris (Sacre Cœur)103287712013-11-29Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttButtes-MontmartreNaNParis48.886172.34515Entire apartmentEntire place21.0["Shampoo", "Heating", "Kitchen", "Essentials", "Washer", "Dryer", "Wifi", "Long term stays allowed"]12021125100.010.010.010.010.010.010.0f
24082273Lovely apartment with Terrace, 60m2192527682014-07-31Paris, Ile-de-France, FranceNaNNaNNaNf1.0tfElyseeNaNParis48.881122.31712Entire apartmentEntire place21.0["Heating", "TV", "Kitchen", "Washer", "Wifi", "Long term stays allowed"]8921125100.010.010.010.010.010.010.0f
34797344Cosy studio (close to Eiffel tower)106683112013-12-17Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttVaugirardNaNParis48.845712.30584Entire apartmentEntire place21.0["Heating", "TV", "Kitchen", "Wifi", "Long term stays allowed"]5821125100.010.010.010.010.010.010.0f
44823489Close to Eiffel Tower - Beautiful flat : 2 rooms248375582014-12-14Paris, Ile-de-France, FranceNaNNaNNaNf1.0tfPassyNaNParis48.855002.26979Entire apartmentEntire place21.0["Heating", "TV", "Kitchen", "Essentials", "Hair dryer", "Washer", "Dryer", "Bathtub", "Wifi", "Elevator", "Long term stays allowed", "Cable TV"]6021125100.010.010.010.010.010.010.0f
54898654NEW - Charming apartment Le Marais5055352011-04-13Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttTempleNaNParis48.864282.35370Entire apartmentEntire place21.0["Heating", "TV", "Kitchen", "Essentials", "Washer", "Smoke alarm", "Wifi", "Long term stays allowed", "Cable TV"]9521125100.010.010.010.010.010.010.0f
660217002P - Entre Bastille et Republique80536902013-08-09Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttPopincourtNaNParis48.863842.37101Entire apartmentEntire place21.0["Shampoo", "TV", "Kitchen", "Washer", "Smoke alarm", "Wifi", "Fire extinguisher", "Long term stays allowed"]8021125100.010.010.010.010.010.010.0f
7694574057sqm btw. Bastille & Père Lachaise59247092013-04-14Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttPopincourtNaNParis48.860432.37842Entire apartmentEntire place21.0["Heating", "TV", "Kitchen", "Essentials", "Washer", "Dryer", "Smoke alarm", "Wifi", "Long term stays allowed"]5921125100.010.010.010.010.010.010.0f
87491966Charming appartment near the Parc Buttes Chaumont358127622015-06-14Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttButtes-ChaumontNaNParis48.878712.37489Entire apartmentEntire place21.0["Paid parking off premises", "Shampoo", "Heating", "TV", "Iron", "Kitchen", "Hair dryer", "Essentials", "Washer", "Hot water", "Hangers", "Smoke alarm", "Wifi", "Long term stays allowed", "Dedicated workspace", "Host greets you", "Cable TV"]8021125100.010.010.010.010.010.010.0f
97849932Bel appartement plein de charme !208332912014-09-02Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttOperaNaNParis48.877902.33122Entire apartmentEntire place21.0["Heating", "TV", "Iron", "Kitchen", "Essentials", "Hair dryer", "Washer", "Hangers", "Wifi", "Elevator", "Long term stays allowed", "Dedicated workspace", "Cable TV"]9021125100.010.010.010.010.010.010.0f
listing_idnamehost_idhost_sincehost_locationhost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_total_listings_counthost_has_profile_pichost_identity_verifiedneighbourhooddistrictcitylatitudelongitudeproperty_typeroom_typeaccommodatesbedroomsamenitiespriceminimum_nightsmaximum_nightsreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueinstant_bookable
27970236694889Appartement charme Haussmanien Alesia.96818302013-10-28Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttObservatoireNaNParis48.827542.32888Entire apartmentEntire place21.0["Shampoo", "Heating", "Washer", "Hair dryer", "Indoor fireplace", "Smoke alarm", "Wifi", "Dedicated workspace", "Kitchen", "Essentials", "Private entrance", "Long term stays allowed", "TV", "Breakfast"]30131100.010.010.010.010.010.010.0f
27970336944049Nid douillet Montorgueil163158922014-06-03Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttBourseNaNParis48.866482.34613Entire apartmentEntire place21.0["Heating", "Washer", "Dryer", "Wifi", "Kitchen", "Dishes and silverware", "First aid kit", "Hot water", "Hangers", "Essentials", "Long term stays allowed", "TV"]80635100.010.010.010.010.010.010.0f
27970437523165Appartement Paris 20ème2724819742019-06-30Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttMenilmontantNaNParis48.859812.40858Entire apartmentEntire place21.0["TV", "Iron", "Hair dryer", "Hangers", "Smoke alarm", "Kitchen", "Coffee maker", "Free parking on premises", "Free street parking", "Host greets you", "Hot water", "Heating", "Essentials", "Dishes and silverware", "Washer", "Microwave", "Wifi"]5057100.010.010.010.010.010.010.0f
27970538008602Charmant appartement parisien sous les toits205040862014-08-25Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttEnclos-St-LaurentNaNParis48.868052.36236Entire apartmentEntire place21.0["Iron", "Heating", "Hair dryer", "Extra pillows and blankets", "Dedicated workspace", "Wifi", "Kitchen", "Essentials", "Long term stays allowed", "Bed linens"]1202365100.010.010.010.010.010.010.0f
27970638111942cocoon, cozy & surf spirit78413632013-07-31Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttBatignolles-MonceauNaNParis48.881172.29204Entire apartmentEntire place21.0["Shampoo", "Heating", "TV", "Iron", "Kitchen", "Essentials", "Hair dryer", "Washer", "Carbon monoxide alarm", "Hangers", "Wifi", "Dishes and silverware"]13515100.010.010.010.010.010.010.0f
27970738338635Appartement T2 neuf près du tram T3a Porte Didot311611812015-04-13Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttObservatoireNaNParis48.827012.31419Entire apartmentEntire place21.0["Iron", "Heating", "Washer", "Dedicated workspace", "Elevator", "Smoke alarm", "Wifi", "Kitchen", "Hot water", "Hangers", "Essentials", "TV"]12017100.010.010.010.010.010.010.0f
27970838538692Cozy Studio in Montmartre102948582013-11-27Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttButtes-MontmartreNaNParis48.893092.33206Entire apartmentEntire place21.0["Shampoo", "Iron", "Heating", "Washer", "Hair dryer", "Elevator", "Wifi", "Kitchen", "Hot water", "Hangers", "Essentials", "TV"]60715100.010.010.010.010.010.010.0f
27970938683356Nice and cosy mini-appartement in Paris22385022012-04-27Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttButtes-MontmartreNaNParis48.886992.34920Entire apartmentEntire place21.0["Paid parking off premises", "Shampoo", "First aid kit", "Heating", "Iron", "Kitchen", "Hair dryer", "Essentials", "Washer", "Breakfast", "Hot water", "Wifi", "Long term stays allowed", "Dedicated workspace", "Host greets you"]50630100.010.010.010.010.010.010.0f
27971039659000Charming apartment near Rue Saint Maur / Oberkampf386336952015-07-16Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttPopincourtNaNParis48.866872.38123Entire apartmentEntire place21.0["TV", "Iron", "Kitchen", "Hangers", "Smoke alarm", "Cable TV", "Dedicated workspace", "Hot water", "Heating", "Shampoo", "Elevator", "Essentials", "Washer", "Carbon monoxide alarm", "Wifi"]105318100.010.010.010.010.010.010.0f
27971140219504Cosy apartment with view on Canal St Martin69556182013-06-17Paris, Ile-de-France, FranceNaNNaNNaNf1.0ttEnclos-St-LaurentNaNParis48.872172.36320Entire apartmentEntire place21.0["Shower gel", "Shampoo", "Iron", "Heating", "Washer", "Dedicated workspace", "Extra pillows and blankets", "Wifi", "Dishes and silverware", "Kitchen", "Refrigerator", "Cooking basics", "Hot water", "Essentials", "Bed linens", "Dishwasher", "Coffee maker"]7024100.010.010.010.010.010.010.0f